In this paper, a kind of skin dressing, agarose- grafting- hyaluronic acid (Ag-g-HA) sponge was applied to test the modified agarose based scaffold for skin regeneration. The bFGF loading agarose-grafting hyaluronan scaffold had homogenous porosities, and the loaded bFGF was bioactive in 2 weeks. The Ag-g-HA sponge was applied into skin of mice, and it was found that the dressing promoted skin regeneration and no infection and leakage in lesion site took place. H&E staining results showed that the repaired skin was similar to autologous skin. These demonstrate that Ag-g-HA sponge has a promise in skin regeneration.
Animals ; Bandages ; Female ; Fibroblast Growth Factor 2 ; physiology ; Hyaluronic Acid ; therapeutic use ; Mice ; Mice, Inbred C57BL ; Polysaccharides ; isolation & purification ; therapeutic use ; Random Allocation ; Seaweed ; chemistry ; Sepharose ; isolation & purification ; therapeutic use ; Surgical Sponges ; Wound Healing ; drug effects ; Wounds and Injuries ; therapy

Objective: To understand the real experience and feelings of self injurious cutting in adolescents with depression, to provide guidance for clinical targeted interventions. Methods: During November 2021 to May 2022, 19 adolescent patients with depression who had cut themselves as the type of non suicidal self injury were recruited from a tertiary first class psychiatric hospital in Chengdu, Sichuan Province. All the participants were interviewed in a semi structured manner, which used the interpretative phenomenological analysis to analyze the transcription data. Results: The experience and feelings of non suicidal self injury in adolescent with depression could be summarized into five themes: self injury thoughts that arise under external interference; self injurious behavior in a thousand thoughts; painful but a happy experience of self injury; cutting as the most frequently selected form of non suicidal self injury; decreases in self injurious behavior reduced when they feel love and responsibility. Conclusion Non suicidal self injury of adolescent patients with depression are affected by various factors. Clinicians should provide targeted clinical care according to the characteristics of patients, as well as the no suicide contract, alternative skills of non suicidal self injury behaviors, and a multi dimensional social support platform with the families of patients.

Objective:To explore the reasons why patients with severe mental disorders and their family members refuse to participate in the community management and treatment service network.Methods:Totally 12 patients with severe mental disorders hospitalized and 7 family members in a ClassⅢ Grade A psychiatric hospital in Chengdu between January and May 2018 received semi-structural interviews using descriptive qualitative research methods. The content analysis and Nvivo 10.0 were used to organize and analyze the interview materials.Results:The five themes that affected refusal of patients with severe mental disorders and their family members to participate in the community management and treatment network were: poor perception of the patients, which covered the sub-themes of insufficient awareness of severe mental disorders and incorrect understanding of the community management and treatment service network; negative emotions of the patients and their family members, including the sub-themes of severe feelings of stigma and lack of trust; the patients and their family members actual needs were not met; the introverted personality traits of the patients; the decision-making game between the patients and the main caregiver of the family.Conclusions:Strengthening publicity, fully protecting the privacy of the patients, eliminating doubts, building a professional psychiatric prevention team, providing a diverse community management and treatment service network, and grasping the relevant drivers for the patient's refusal to participate in the community management and treatment service network can help enhance the enthusiasm of patients with severe mental disorders and their family members to participate in the community management and treatment service network.

Objective To evaluate diagnostic method and treatment strategy for free floating thrombus in carotid artery.Methods From Ju12016 to Oct 2017,7 patients with free floating thrombus in carotid artery was diagnosed at our department.The medical history,symptoms,diagnosis,treatment strategy and prognosis of those patients were analyzed retrospectively.Results Among 7 patients,4 were symptomatic;4 patients were concomitant with severe carotid artery stenosis and 3 with mild to moderate stenosis.3 received carotid endarterectomy and patch angioplasty.4 received carotid artery stenting with distal cerebral protection divice.There was no perioperative and 30-day stroke,myocardial infarction,death or hyperperfusion syndrome occurred.The 12-month follow up showed no restenosis,no free floating thrombus recurrence and no ischemic cerebrovascular event.Conclusion Free floating filling defect in carotid artery is a typical sign for unstable plaque.Both carotid endarterectomy and carotid artery stenting can be used for the treatment of free floating thrombus.

Objective To explore the effect of angiogenesis in carotid atherosclerotic plaque.Methods From Jan 2016 to Aug 2016,Carotid artery plaque was abtained in 52 cases after carotid endarterectomy at the Department of Cardiovascular Surgery of China-Japan Friendship Hospital.Patients were divided into symptomatic group and asymptomatic group.Specimens were stained with HE and Movat,and the density,size,distribution and morphology of neovascularization were counted.Results The density of neovascularization in the symptomatic group and the asymptomatic group were 5.27 ± 0.46 and 2.30 ±0.29,respectively (P ＜ 0.001),the average cross-sectional area of neovascularization in the symptomatic group was (2.26±0.21) mm2 and (1.00 ±0.48) mm2 in the asymptomatic group (P=0.02).In the distribution,the symptomatic group and the asymptomatic group were 3.37 ± 0.46/ mm2,1.32 ±0.16/mm2 in basal part,3.71 ±0.42/mm2,1.56 ±0.20/mm2 in the shoulder part,3.48 ±0.44/mm2,1.55 ± 0.21/ mm2 in the fibrous cap,respectively (P ＜ 0.001).Conclusion The density and cross-sectional area of neovascularization in the symptomatic group were larger than those in the asymptomatic group,irregular branching vessels were dominant.

Objective    To explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). Methods    The clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). Results    Four radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. Conclusion    The machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.

Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Objective     To identify the preoperative risk factors for prolonged mechanical ventilation (PMV) after pulmonary thromboendarterectomy (PTE). Methods    The clinical data of patients who underwent PTE from December 2016 to August 2021 in our hospital were retrospectively analyzed. The patients were divided into two groups according to the postoperative mechanical ventilation time, including a postoperative mechanical ventilation time≤48 h group (≤48 h group) and a postoperative mechanical ventilation time>48 h (PMV) group (>48 h group). Univariable and logistic regression analysis were used to identify the preoperative risk factors for postoperative PMV. Results    Totally, 90 patients were enrolled in this study. There were 40 patients in the ≤48 h group, including 30 males and 10 females, with a mean age of 45.48±12.72 years, and there were 50 patients in the >48 h group, including 29 males and 21 females, with a mean age of 55.50±10.42 years. The results showed that in the ≤48 h group, the median postoperative ICU stay was 3.0 days, and the median postoperative hospital stay was 15.0 days; in the >48 h group, the median postoperative ICU stay was 7.0 days, and the median postoperative hospital stay was 20.0 days. The postoperative PMV was significantly correlated with tricuspid annular plane systolic excursion (TAPSE) [OR=0.839, 95%CI (0.716, 0.983), P=0.030], age [OR=1.082, 95%CI (1.034, 1.132), P=0.001] and pulmonary vascular resistance (PVR) [OR=1.001, 95%CI (1.000, 1.003), P=0.028]. Conclusion    Age and PVR are the preoperative risk factors for PMV after PTE, and TAPSE is the preoperative protective factor for PMV after PTE.